Pose Estimation Similar to [1]}, rather than directly regress the rotation matrix \(R\) , it is more effective to decouple it into two orthogonal axes and estimate them separately.
As shown in Figure REF , we decouple \(R\)  into the \(z\) -axis \(a_z\)  (red axis) and \(x\) -axis \(a_x\)  (green axis).
Following the strategy of confidence learning in [2]}, the network learns confidence values to deal with the problem that the regressed two axes are not orthogonal:
\(\begin{split}&\left[\hat{a}_i, c_i\right] = \mathcal {F}_i\left(\mathcal {P}_{concat}\right), \ i\in \left\lbrace x, z\right\rbrace  \\&\theta _z = \frac{c_x}{c_x + c_z}\left(\theta - \frac{\pi }{2}\right)\\&\theta _x = \frac{c_z}{c_x + c_z}\left(\theta - \frac{\pi }{2}\right)\end{split}\) 
